Human pose estimation in physiotherapy fitness exercise correction using novel transfer learning approach
The proposed RFL method introduces a novel feature generation approach enhancing the accuracy of physical therapy exercise classification and correction. This innovative integration not only improves rehabilitation monitoring but also paves the way for more adaptive and intelligent physiotherapy assistance systems. By leveraging sensor data and advanced machine learning techniques, it has the potential to mitigate risks associated with disabilities and major diseases, thereby offering a feasible alternative to frequent clinic visits for consistent therapist guidance.
Authors
- Muhammad Syafrudin (ORCID: https://orcid.org/0000-0002-5640-4413)
- Yeong Hyeon Gu (ORCID: https://orcid.org/0000-0002-0002-9386)
- Norma Latif Fitriyani (ORCID: https://orcid.org/0000-0002-1133-3965)
- H. M. Rehan Afzal (ORCID: https://orcid.org/0000-0002-1759-2351)
- Ali Raza (ORCID: https://orcid.org/0000-0001-5429-9835)
- Aisha Naseer (ORCID: https://orcid.org/0009-0007-7013-8821)
- Aseel Smerat (ORCID: https://orcid.org/0009-0008-4600-509X)
Institutions
- Al-Ahliyya Amman University (JO)
- University of Lahore (PK)
- Mustansiriyah University (IQ)
- Sejong University (KR)
- Khwaja Fareed University of Engineering and Information Technology (PK)
- Chitkara University (IN)
Publication Details
- Journal
- PeerJ Computer Science
- Published
- 2025-04-29
- DOI
- https://doi.org/10.7717/peerj-cs.2854
- Citations
- 2
- Primary Topic
- Human Pose and Action Recognition
- Type
- article
- Field-Weighted Citation Impact
- 2.55